From 54931dd4e1a099d7d6f144c4e12e5453deb3aa26 Mon Sep 17 00:00:00 2001 From: 雾聪 <wucong.lyb@alibaba-inc.com> Date: 星期三, 28 六月 2023 10:41:57 +0800 Subject: [PATCH] Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into main --- docs/m2met2/Baseline.md | 7 ++++--- 1 files changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/m2met2/Baseline.md b/docs/m2met2/Baseline.md index a782086..adc5307 100644 --- a/docs/m2met2/Baseline.md +++ b/docs/m2met2/Baseline.md @@ -5,8 +5,8 @@  ## Quick start -To run the baseline, first you need to install FunASR and ModelScope. ([installation](https://alibaba-damo-academy.github.io/FunASR/en/installation.html)) -There are two startup scripts, `run.sh` for training and evaluating on the old eval and test sets, and `run_m2met_2023_infer.sh` for inference on the new test set of the Multi-Channel Multi-Party Meeting Transcription 2.0 ([M2MET2.0](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)) Challenge. +To run the baseline, first you need to install FunASR and ModelScope. ([installation](https://github.com/alibaba-damo-academy/FunASR#installation)) +There are two startup scripts, `run.sh` for training and evaluating on the old eval and test sets, and `run_m2met_2023_infer.sh` for inference on the new test set of the Multi-Channel Multi-Party Meeting Transcription 2.0 ([M2MeT2.0](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)) Challenge. Before running `run.sh`, you must manually download and unpack the [AliMeeting](http://www.openslr.org/119/) corpus and place it in the `./dataset` directory: ```shell dataset @@ -16,6 +16,7 @@ |鈥斺�� Test_Ali_near |鈥斺�� Train_Ali_far |鈥斺�� Train_Ali_near +``` Before running `run_m2met_2023_infer.sh`, you need to place the new test set `Test_2023_Ali_far` (to be released after the challenge starts) in the `./dataset` directory, which contains only raw audios. Then put the given `wav.scp`, `wav_raw.scp`, `segments`, `utt2spk` and `spk2utt` in the `./data/Test_2023_Ali_far` directory. ```shell data/Test_2023_Ali_far @@ -30,4 +31,4 @@ ## Baseline results The results of the baseline system are shown in Table 3. The speaker profile adopts the oracle speaker embedding during training. However, due to the lack of oracle speaker label during evaluation, the speaker profile provided by an additional spectral clustering is used. Meanwhile, the results of using the oracle speaker profile on Eval and Test Set are also provided to show the impact of speaker profile accuracy. - \ No newline at end of file + \ No newline at end of file -- Gitblit v1.9.1